Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity

The OMiCC Jamboree Working Group

Research output: Contribution to journalComment/debate

Abstract

Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups. Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures). Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions. Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a "crowd" of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.

Original languageEnglish (US)
Article number2884
JournalF1000Research
Volume5
DOIs
StatePublished - 2016

Fingerprint

Autoimmunity
Meta-Analysis
Crowdsourcing
Gene expression
Genes
Computational Biology
Gene Expression
Bioinformatics
Bioelectric potentials
Type 1 Diabetes Mellitus
Transcriptome
Systemic Lupus Erythematosus
Medical problems
Interferons
Autoimmune Diseases
Multiple Sclerosis
Biomedical Research
Volunteers
Rheumatoid Arthritis
Exercise

Keywords

  • Autoimmunity
  • Crowdsourcing
  • Gene expression
  • Human andmouse comparison
  • Meta-analysis
  • Mouse modelsof disease
  • Public data

ASJC Scopus subject areas

  • Medicine(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

Cite this

Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity. / The OMiCC Jamboree Working Group.

In: F1000Research, Vol. 5, 2884, 2016.

Research output: Contribution to journalComment/debate

@article{afb6d4d81077484ca72f6a15315ada97,
title = "Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity",
abstract = "Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups. Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures). Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions. Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a {"}crowd{"} of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.",
keywords = "Autoimmunity, Crowdsourcing, Gene expression, Human andmouse comparison, Meta-analysis, Mouse modelsof disease, Public data",
author = "{The OMiCC Jamboree Working Group} and Lau, {William W.} and Rachel Sparks and Tsang, {John S.} and James Austin and Neha Bansal and Juli{\'a}n Candia and Ehren Dancy and Elkins, {Karen L.} and Sara Faghihi-Kashani and Julio Gomez-Rodriguez and Liliana Guedez and Yongjian Guo and Gutierrez, {Maria J.} and Maria Gutierrez and Reiko Horai and Sunmee Huh and Chie Iwamura and Jaimy Joy and Kang, {Ju Gyeong} and Sunil Kaul and Lewandowski, {Laura B.} and Candace Liu and Yong Lu and Manes, {Nathan P.} and Mattapallil, {Mary J.} and Sarfraz Memon and {Jubayer Rahman}, M. and Rodrigues, {Kameron B.} and Bruno Silva and Amit Singh and {St. Leger}, {Anthony J.} and Jessica Tang and Abigail Thorpe and Hang Xie and Yongge Zhao and Ofer Zimmerman",
year = "2016",
doi = "10.12688/f1000research.10465.1",
language = "English (US)",
volume = "5",
journal = "F1000Research",
issn = "2046-1402",
publisher = "F1000 Research Ltd.",

}

TY - JOUR

T1 - Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity

AU - The OMiCC Jamboree Working Group

AU - Lau, William W.

AU - Sparks, Rachel

AU - Tsang, John S.

AU - Austin, James

AU - Bansal, Neha

AU - Candia, Julián

AU - Dancy, Ehren

AU - Elkins, Karen L.

AU - Faghihi-Kashani, Sara

AU - Gomez-Rodriguez, Julio

AU - Guedez, Liliana

AU - Guo, Yongjian

AU - Gutierrez, Maria J.

AU - Gutierrez, Maria

AU - Horai, Reiko

AU - Huh, Sunmee

AU - Iwamura, Chie

AU - Joy, Jaimy

AU - Kang, Ju Gyeong

AU - Kaul, Sunil

AU - Lewandowski, Laura B.

AU - Liu, Candace

AU - Lu, Yong

AU - Manes, Nathan P.

AU - Mattapallil, Mary J.

AU - Memon, Sarfraz

AU - Jubayer Rahman, M.

AU - Rodrigues, Kameron B.

AU - Silva, Bruno

AU - Singh, Amit

AU - St. Leger, Anthony J.

AU - Tang, Jessica

AU - Thorpe, Abigail

AU - Xie, Hang

AU - Zhao, Yongge

AU - Zimmerman, Ofer

PY - 2016

Y1 - 2016

N2 - Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups. Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures). Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions. Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a "crowd" of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.

AB - Background: The proliferation of publicly accessible large-scale biological data together with increasing availability of bioinformatics tools have the potential to transform biomedical research. Here we report a crowdsourcing Jamboree that explored whether a team of volunteer biologists without formal bioinformatics training could use OMiCC, a crowdsourcing web platform that facilitates the reuse and (meta-) analysis of public gene expression data, to compile and annotate gene expression data, and design comparisons between disease and control sample groups. Methods: The Jamboree focused on several common human autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis (MS), type I diabetes (DM1), and rheumatoid arthritis (RA), and the corresponding mouse models. Meta-analyses were performed in OMiCC using comparisons constructed by the participants to identify 1) gene expression signatures for each disease (disease versus healthy controls at the gene expression and biological pathway levels), 2) conserved signatures across all diseases within each species (pan-disease signatures), and 3) conserved signatures between species for each disease and across all diseases (cross-species signatures). Results: A large number of differentially expressed genes were identified for each disease based on meta-analysis, with observed overlap among diseases both within and across species. Gene set/pathway enrichment of upregulated genes suggested conserved signatures (e.g., interferon) across all human and mouse conditions. Conclusions: Our Jamboree exercise provides evidence that when enabled by appropriate tools, a "crowd" of biologists can work together to accelerate the pace by which the increasingly large amounts of public data can be reused and meta-analyzed for generating and testing hypotheses. Our encouraging experience suggests that a similar crowdsourcing approach can be used to explore other biological questions.

KW - Autoimmunity

KW - Crowdsourcing

KW - Gene expression

KW - Human andmouse comparison

KW - Meta-analysis

KW - Mouse modelsof disease

KW - Public data

UR - http://www.scopus.com/inward/record.url?scp=85006816750&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006816750&partnerID=8YFLogxK

U2 - 10.12688/f1000research.10465.1

DO - 10.12688/f1000research.10465.1

M3 - Comment/debate

C2 - 28491277

AN - SCOPUS:85006816750

VL - 5

JO - F1000Research

JF - F1000Research

SN - 2046-1402

M1 - 2884

ER -